The analysis of blood components is an important diagnostic tool for better understanding the physical condition of a patient. Presently, adequate noninvasive blood analysis technology is not currently available and blood samples still need to be obtained by invasive methods from a great number of patients every day for analysis. A well known example of such needs is the monitoring of glucose levels by a diabetic individual. Similarly, concentration of other physiological chemicals are important for determining the health condition of some individuals.
Research effort has been directed to non-invasive analysis of blood chemicals. Take the example of glucose. Glucose has several absorption peaks in the near infrared and the far infrared. Researchers have made progress in measuring glucose in solution by means of absorption of such radiation. Unfortunately, water, which is a major component of tissue and blood also, absorbs heavily in most of these regions. This makes it almost impossible to extract glucose information by absorption in most of these regions. However, in the near infrared some weak absorption bands of glucose overlap valleys in the water absorption bands. These bands, being weak absorption bands in the vicinity of huge water bands, are extremely difficult to analyze, particularly in complex systems such as tissue where several other analytes are also present and are themselves fluctuating. Successful glucose analysis therefore requires separating a weak signal in the midst of influences of chemical interference and temperature and flow related fluctuations.
Several techniques for processing the spectral data to eliminate these influences have been developed. Multivariate regression analysis such as PLS (partial least square) methods and PCR (principal component resolution) methods have been widely applied. More recently, Fourier filtering of the spectral data followed by multivariate regression analysis have been used to improve the prediction of glucose in biological samples. However, we have found that when the temperature of even simple aqueous samples varies over the human body temperature range, the predictive ability of these methods can be seriously reduced. Further, such prior techniques may not be robust enough to determine accurately the glucose composition when some of the interfering species such as proteins in the sample fluctuate over the normal range for human blood. Therefore, a better method of analyzing such data is required.